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DigitHist: a Histogram-Based Data Summary with Tight Error Bounds

Summary: DigitHist: a histogram-based data summary for multi-dimensional selectivity with tight error bounds. Hybrid 1D/2D grids with sparse encoding and adaptive resolution; introduces u-error to minimize the gap between bounds; single-pass linear-time construction; superior precision at similar query time. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11623
Venue
VLDB
Year
2017
Pagerank
6.7218674e-05
Overall Rank
4,405 | 69.78%
DOI
10.14778/3137628.3137658

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Authors

BibTeX Citation

@article{shekelyan_vldb17,
        title = {{DigitHist: a Histogram-Based Data Summary with Tight Error Bounds}},
        author = {Shekelyan, Michael and Dignös, Anton and Gamper, Johann},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {11},
        doi = {10.14778/3137628.3137658},
        url = {https://doi.org/10.14778/3137628.3137658},
        year = {2017}
}

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